Gemini 3.1 Flash Lite Preview vs GPT-5 Nano

Across our 12-test suite, Gemini 3.1 Flash Lite Preview wins six categories: strategic_analysis (5 vs 4), constrained_rewriting (4 vs 3), creative_problem_solving (4 vs 3), faithfulness (5 vs 4), safety_calibration (5 vs 4), and persona_consistency (5 vs 4). Context: Gemini ties for 1st on faithfulness, strategic_analysis, persona_consistency, and multilingual (see rankingsA: faithfulness rank 1 tied with 32 others; strategic_analysis rank 1 tied with 25; persona_consistency rank 1 tied with 36), and constrained_rewriting places well (rank 6 of 53). GPT-5 Nano wins long_context (5 vs 4) and ranks tied for 1st on long_context (rank 1 of 55 tied with 36 others), making it stronger for retrieval and 30K+ token scenarios. Five tests are ties: structured_output (both 5), tool_calling (both 4, rank 18 of 54), classification (both 3), agentic_planning (both 4), and multilingual (both 5). Notable external data: GPT-5 Nano scores 95.2% on MATH Level 5 and 81.1% on AIME 2025 (Epoch AI), indicating strong compact-math performance on those third-party benchmarks. In practice this means: choose Gemini when you need strict refusal behavior, fidelity to source material, robust persona enforcement, or higher-level strategic reasoning; choose GPT-5 Nano when you need maximum context handling and a much lower per-token bill. Tool-calling and structured-output behavior are comparable between them in our tests.

Per the payload, Gemini 3.1 Flash Lite Preview costs $0.25 input + $1.50 output per million tokens (total $1.75 per M-token if you sum input+output). GPT-5 Nano costs $0.05 input + $0.40 output per M-token (total $0.45 per M-token). If you assume an equal split of input vs output tokens, the per-million effective costs are $0.875 (Gemini) vs $0.225 (GPT) and scale linearly: for 1M/10M/100M total tokens (equal split), Gemini ≈ $0.88 / $8.75 / $87.50 and GPT-5 Nano ≈ $0.23 / $2.25 / $22.50. High-volume services (10M–100M tokens/month) will see the gap multiply into tens to hundreds of dollars monthly; teams delivering large-scale chat, analytics, or API-driven user experiences should care — GPT-5 Nano saves roughly 3.75x on raw per-token spend (payload priceRatio = 3.75).

Choose Gemini 3.1 Flash Lite Preview if you prioritize safety calibration, faithfulness, persona consistency, or strategic reasoning (the model wins 6 of 12 internal tests and ties for top ranks in several). Choose GPT-5 Nano if you need long-context retrieval (tied for 1st on long_context) or are optimizing for cost — GPT-5 Nano’s token pricing is ~3.75x cheaper per the payload. Specific picks: use Gemini for regulated chatbots, customer support with strict refusal rules, and instruction-following agents; use GPT-5 Nano for cost-sensitive production tooling, large-scale retrieval/archival Q&A, or math-focused microservices (see its 95.2% MATH Level 5 and 81.1% AIME 2025 scores from Epoch AI).